MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video

• URL: http://arxiv.org/abs/2312.00778v2
• Date: Thu, 4 Apr 2024 13:00:20 GMT
• Title: MorpheuS: Neural Dynamic 360° Surface Reconstruction from Monocular RGB-D Video
• Authors: Hengyi Wang, Jingwen Wang, Lourdes Agapito,
• Abstract summary: We introduce MorpheuS, a framework for dynamic 360deg surface reconstruction from a casually captured RGB-D video. Our approach models the target scene as a canonical field that encodes its geometry and appearance. We leverage a view-dependent diffusion prior and distill knowledge from it to achieve realistic completion of unobserved regions.
• Score: 14.678582015968916
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Neural rendering has demonstrated remarkable success in dynamic scene reconstruction. Thanks to the expressiveness of neural representations, prior works can accurately capture the motion and achieve high-fidelity reconstruction of the target object. Despite this, real-world video scenarios often feature large unobserved regions where neural representations struggle to achieve realistic completion. To tackle this challenge, we introduce MorpheuS, a framework for dynamic 360{\deg} surface reconstruction from a casually captured RGB-D video. Our approach models the target scene as a canonical field that encodes its geometry and appearance, in conjunction with a deformation field that warps points from the current frame to the canonical space. We leverage a view-dependent diffusion prior and distill knowledge from it to achieve realistic completion of unobserved regions. Experimental results on various real-world and synthetic datasets show that our method can achieve high-fidelity 360{\deg} surface reconstruction of a deformable object from a monocular RGB-D video.

Related papers

• SpectroMotion: Dynamic 3D Reconstruction of Specular Scenes [7.590932716513324]
  We present SpectroMotion, a novel approach that combines 3D Gaussian Splatting (3DGS) with physically-based rendering (PBR) and deformation fields to reconstruct dynamic specular scenes.
  arXiv  Detail & Related papers  (2024-10-22T17:59:56Z)
• Dynamic Scene Understanding through Object-Centric Voxelization and Neural Rendering [57.895846642868904]
  We present a 3D generative model named DynaVol-S for dynamic scenes that enables object-centric learning. voxelization infers per-object occupancy probabilities at individual spatial locations. Our approach integrates 2D semantic features to create 3D semantic grids, representing the scene through multiple disentangled voxel grids.
  arXiv  Detail & Related papers  (2024-07-30T15:33:58Z)
• MonoNPHM: Dynamic Head Reconstruction from Monocular Videos [47.504979561265536]
  We present Monocular Neural Parametric Head Models (MonoNPHM) for dynamic 3D head reconstructions from monocular RGB videos. We constrain predicted color values to be correlated with the underlying geometry such that gradients from RGB effectively influence latent geometry codes during inverse rendering.
  arXiv  Detail & Related papers  (2023-12-11T17:55:05Z)
• NeRF-DS: Neural Radiance Fields for Dynamic Specular Objects [63.04781030984006]
  Dynamic Neural Radiance Field (NeRF) is a powerful algorithm capable of rendering photo-realistic novel view images from a monocular RGB video of a dynamic scene. We address the limitation by reformulating the neural radiance field function to be conditioned on surface position and orientation in the observation space. We evaluate our model based on the novel view synthesis quality with a self-collected dataset of different moving specular objects in realistic environments.
  arXiv  Detail & Related papers  (2023-03-25T11:03:53Z)
• Dynamic Multi-View Scene Reconstruction Using Neural Implicit Surface [0.9134661726886928]
  We propose a template-free method to reconstruct surface geometry and appearance using neural implicit representations from multi-view videos. We leverage topology-aware deformation and the signed distance field to learn complex dynamic surfaces via differentiable volume rendering. Experiments on different multi-view video datasets demonstrate that our method achieves high-fidelity surface reconstruction as well as photorealistic novel view synthesis.
  arXiv  Detail & Related papers  (2023-02-28T19:47:30Z)
• NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos [82.74918564737591]
  We present a method for learning 3D geometry and physics parameters of a dynamic scene from only a monocular RGB video input. Experiments show that our method achieves superior mesh and video reconstruction of dynamic scenes compared to competing Neural Field approaches.
  arXiv  Detail & Related papers  (2022-10-22T04:57:55Z)
• Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera [26.410460029742456]
  We propose a template-free method to recover high-fidelity geometry and motions of a dynamic scene from a monocular RGB-D camera. Experiments on public datasets and our collected dataset demonstrate that NDR outperforms existing monocular dynamic reconstruction methods.
  arXiv  Detail & Related papers  (2022-06-30T13:09:39Z)
• Unbiased 4D: Monocular 4D Reconstruction with a Neural Deformation Model [76.64071133839862]
  Capturing general deforming scenes from monocular RGB video is crucial for many computer graphics and vision applications. Our method, Ub4D, handles large deformations, performs shape completion in occluded regions, and can operate on monocular RGB videos directly by using differentiable volume rendering. Results on our new dataset, which will be made publicly available, demonstrate a clear improvement over the state of the art in terms of surface reconstruction accuracy and robustness to large deformations.
  arXiv  Detail & Related papers  (2022-06-16T17:59:54Z)
• Learning Dynamic View Synthesis With Few RGBD Cameras [60.36357774688289]
  We propose to utilize RGBD cameras to synthesize free-viewpoint videos of dynamic indoor scenes. We generate point clouds from RGBD frames and then render them into free-viewpoint videos via a neural feature. We introduce a simple Regional Depth-Inpainting module that adaptively inpaints missing depth values to render complete novel views.
  arXiv  Detail & Related papers  (2022-04-22T03:17:35Z)
• Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video [76.19076002661157]
  Non-Rigid Neural Radiance Fields (NR-NeRF) is a reconstruction and novel view synthesis approach for general non-rigid dynamic scenes. We show that even a single consumer-grade camera is sufficient to synthesize sophisticated renderings of a dynamic scene from novel virtual camera views.
  arXiv  Detail & Related papers  (2020-12-22T18:46:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.